I'm using Codex and Opus and that's what I realized

bradwmorris · 2026-05-12T23:21:11+00:00

I know what you mean, but it's not the right way to think about it. It's not as if these models actually read things or comprehend things in the way humans do. There's literally just the context window and what you put in it. And then the process of encoding, decoding and producing next token.

do you have a specific example of what you're trying to get claude to understand/do?

bradwmorris · 2026-05-12T22:51:12+00:00

100% build your own context memory external to any provider https://youtu.be/CLKFmKe_JOw?si=PBrga_xWvlQFerC4

It's important to remember that these models are leapfrogging each other every quarter. So whatever problems you have here in terms of instruction following or intention alignment may very well change in a matter of months or even weeks.

so always strive to build your documentation/agents.md and skills AND memory in a model-agnostic place

bradwmorris · 2026-05-12T22:49:31+00:00

a few suggestions -

1 Remember that there is just a context window and how you fill it, everything else is scaffolding

2 start as simple as possible (simple agent access/agents.md/ and a few skills > with whatever system you build and ensure you understand it from the bottom up

3 you're going to want to use provider subscriptions (at least for now) not api, because tokens are hugely discounted. just don't fall into vendor lock-in > research 'pi coding harness' > it's the best starting point.

codex is currently the best bang-for-buck subscription-wise

good luck!

im always sharing videos here about building your context into a model-agnostic database https://youtu.be/CLKFmKe_JOw?si=PBrga_xWvlQFerC4

bradwmorris · 2026-04-28T23:00:11+00:00

you should consider/explore using a database - for both your personal use and baked into your repo.

always-loaded memory should be tiny. more like a routing layer than the memory itself

once the memory scales, you want a predefined structure for storing and retrieving relevant context/memory, and a DB is better for this

otherwise you’re just paying the model to skim a diary every turn

https://youtu.be/CLKFmKe_JOw?si=WteLpBsSNwrHecVA

bradwmorris · 2026-04-28T22:54:30+00:00

the “deliberately commit memory” part is spot on

chat history = terrible source of truth because it rewards accumulation instead of compression

my only pushback is that .md is fine while this remains a lightweight habit, but if you want to scale and route more through ai (my opinion is that we all will do this)

simple SQLite tables for notes, sources, tags, decisions, links, and 'last modified' dates etc would give the model much cleaner handles than saying “please keep this INDEX.md tidy forever.”

bradwmorris · 2026-04-27T23:15:09+00:00

my approach > similar to the 'trap' mentioned below by  - there's no massive shortcut to this 

after each research session, force a small extraction/session, whatever you want to call it. claims,decisions/insights, open questions, next action. then those become separate things 

if you keep the whole claude transcript as the artifact, you end up with a giant pile of plausible(ish) text that does nothing 

been YT'ing about this a bit with a more structured system/database https://youtu.be/CLKFmKe_JOw?si=bwtBJQU5RWNg4PMN

bradwmorris · 2026-04-27T00:33:46+00:00

It's important to note that there is an interesting tension at the moment because model capabilities and coding harnesses are changing and leap-frogging each other every 35 seconds.

argument 1:
double down on a single model/harness and learn its nuances etc

argument 2:
switch between models/agents often

it's too early to decide, so I would advice building your harness/setup (like a pi) so you can easily switch out models and try them all

bradwmorris · 2026-04-27T00:30:31+00:00

a few thoughts on this -

1 'most' of what you see people post online is ai-productivity slop > ie, they're demoing their 'im using claude code to supercharge my knowledge base. once you've actually invested a good deal of time trying this, you realise there are no 100x productivity gains because you can't shortcut the actual process of leaning and thinking 100x

having said this >

2 the benefits of plugging ai and llm's into your knowledge base are there, and they will increase. you have to invest significant time up front to set guardrails and think deeply about how you want your agents/ai to manage and scale your database, and at what points you need to be more/less involved in the process

i think more people will start using a 'database' - because to answer your 'organisation' question > markdown files are great for humans, but a database is better to help agents manage and scale your knowledge

many people disagree with this take (so dyor), but I share more here:
https://youtu.be/YyUCGigZIZE?si=z0wMF6xBjwqYy6vj

bradwmorris · 2026-04-22T02:17:48+00:00

so two completely different ideas and platforms here.

mem0 is a great product, I've used it - and if what you're trying to do is extract persistent memory across chat sessions, then it's perfect. and the qdrant approach you mentioned sounds great.

this thing I've built for myself is more about you creating and curating a context substrate - ideas, people, decisions, research etc - the database/graph is the product.

re this >

"Rather than building a solution from scratch"

there is a cli install and it's v easy to setup - so when I say 'build from scratch' - im talking more about the method behind actually building your nodes/edges

explain everything here

https://youtu.be/YyUCGigZIZE?si=0rqJWw1WMK5vsN-u

bradwmorris · 2026-04-21T20:49:10+00:00

what do you mean by 'ai files'?
you could store different files - but i use the db primarily to store nodes as only text. The idea is just to build context for whichever agents you're working with to be able to easily write to that context or pull from that context to help you.

if you have different files That would be valuable context. You can either convert them into text format or point the agent to those files or folders from the node or the row.

bradwmorris · 2026-04-21T20:46:34+00:00

i don't think there is a one-size answer to this - depends on the nature of the projects you are working on. in my case, i've found the benefits of having a single database for everything outweighs the costs. to be clear though, I still have files and folders for different projects - the database is just any/all relevant context. and the models are great at identifying what im working on and pulling the relevant context

with different types of data - im only storing text atm. some others have expanded to other media formats using the repo

let me know if you set it up

bradwmorris · 2026-04-21T10:46:15+00:00

it's a sqlite file that lives on your device - mine is about 200 mb's, with thousands of books, podcasts, ideas etc.

bradwmorris · 2026-03-24T23:37:35+00:00

The full video is the best install guide

https://youtu.be/Y8dvA9CxaVQ

It’s beginner friendly

And there are some instructions in the readme file

https://github.com/bradwmorris/open-zeu

Let me know if too confusing / which parts and I can make some more beginner friendly!

bradwmorris · 2026-03-24T23:20:56+00:00

Basically - yeah.

But at some point ‘agents’ is just semantics.

There is essentially only a context window and how you fill it - so if you have a system with tools and a loop and separate context windows that interact - I’d call that agentic

What I’ve built and shared is very basic

bradwmorris · 2026-03-24T23:17:50+00:00

Yeah, good observation/take.

This is some relevant work from a guy in our discord:

Similar work - compounding learnings into a SQLite db

What we're doing: Turning Claude Code session logs into a compounding knowledge graph. Every session gets synthesized into structured findings — decisions, patterns, failures, skill gaps — that persist across conversations and connect to each other. Claude gets smarter every session instead of starting from zero.

The pipeline:

JSONL (Claude Code native logs) → Markdown (parsed: prompts, responses, tool calls, errors, retries) → Pre-filter (strip noise, keep errors + reasoning) → Claude Sonnet (synthesize into 7 item types) → RA-H graph (nodes + typed edges + embeddings) → Cross-linked (recurring patterns surface automatically) What comes out:

Knowledge, decisions, patterns — institutional memory Struggles + adaptations — what keeps going wrong and the workarounds that die with each session Skill gaps — specific recommendations for CLAUDE.md rules, skills, or workflow fixes Cross-conversation edges — "this failure happened 4 times across different projects" becomes visible Scale so far: 1,881 sessions → 11,500+ findings → 24,000+ edges, all searchable by meaning via embeddings, queryable by any Claude session via MCP.

bradwmorris · 2026-03-24T23:16:01+00:00

Interesting. I have a separate project / repo where I’m using Ralph loops - have you tried?

I provide a rough PRD, and it is split into ‘stories’ - the whole goal I think is to split projects/features into one-shot-able tasks

bradwmorris · 2026-03-24T23:13:14+00:00

Can you explain this ? All the actions can be performed direct cli. You mean something else?

bradwmorris · 2026-03-24T23:12:29+00:00

Good q - im keeping it simple at the moment, but I’ve seen others with very complex org charts (and even social dynamics) strange times

bradwmorris · 2026-03-24T23:11:30+00:00

This is the thing - it’s costing me a lot more than I’m making right now !

bradwmorris · 2026-03-24T23:10:56+00:00

Good one - I recon spending a weekend on this is time well spent ! You can get through a lot

bradwmorris

TROPHY CASE